Human cytomegalovirus (HCMV) is a herpesvirus infecting up to 90% of the adult population. Exposure results in a lifelong chronic infection with the virus replicating in several cell types within the body. The virus causes severe disease in immunosuppressed patients and is the leading viral cause of congenital birth defects. No vaccines are available, and although antiviral therapies exist, most exhibit a high degree of toxicity and emergence of antiviral resistance. HCMV infection requires viral manipulation of the cell cycle creating a pseudo- cycle characterized by altered activities of many cellular proteins. Due to the complexity of the cell cycle regulatory network, computational modeling is extremely helpful in simulating in silico the oscillating changes and interrelationships between multiple cellular variables (protein activities) under normal conditions, and how they are altered leading to pseudo-cycle creation in infection. Our short-term goal is to develop computer simulations of the mitotic cycle to assist us in identifying which HCMV-manipulated cellular variables are critical for inducing a pseudo-mitotic phase defined by a unique mitotic collapse required for virion egress. Our long- term goal is to simulate all phases of the cell cycle under normal conditions and how they are dysregulated in infection in varying cell types. Such models will simulate the behavior of the complex cellular regulatory networks based on putative relationships and will inform us of the most plausible mechanisms within the networks. The predictive nature of our models will also guide us in identifying new targeted antivirals and/or strategies to inhibit infection. HCMV infection-induced changes result in a unique mitotic collapse defined by low yet sustained CCNB1:CDK1 activity that is necessary to support HCMV virion egress. These changes are also key determinants in the efficacy of antiviral compounds, Maribavir. We hypothesize that a unique combinatorial set of virus-mediated molecular events are required to construct a unique mitotic collapse and, by defining the required network mechanisms, we will be able to identify in silico and experimentally test key relationships that prevent viral replication. To test this hypothesis, we will develop a computational model of the normal mitotic cycle and incorporate key viral protein interactions to simulate responses to infection, validated by in-house experiments as well as published data under diverse cellular perturbations, including HCMV infection. Using this model, we will identify the cellular variables that are critical in altering the cell cycle into a unique mitotic collapse. We will also be able to infer antiviral efficacy based upon simulating a limited amount of experimental data. We will accomplish our goals by developing a predictive computational model of the normal CCNB1-CDK1 oscillation and its dysregulation during infection (Aim 1) and integrating viral protein function and manipulation of the CDK inhibitor p21 into the computational model of HCMV-induced pseudo-mitotic cycle and virus production (Aim 2). These studies will result in a novel computational model that simulates the mitotic cycle for the purpose of understanding how this network is altered during viral pathogenesis and devising antiviral strategies.